Contrastive virtual staining enhances deep learning-based PDAC subtyping from H&E-stained tissue cores.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) subtyping typically relies on immunohistochemistry (IHC) staining for critical markers like HNF1A and KRT81, a labor-intensive manual staining process that introduces variability. Virtual staining methods offer promising alternatives by generating synthetic IHC images from routine hematoxylin and eosin (H&E) slides. However, most current approaches evaluate success by image quality measures rather than assessing diagnostically relevant features. Here, we introduce a novel cycleGAN framework utilizing a contrastive-inspired approach trained on semipaired datasets derived from consecutive tissue sections. Our method significantly enhances PDAC subtyping accuracy based on synthetic IHC images generated from standard H&E inputs, improving the classificat

SUBMITTER: Fischer M 

PROVIDER: S-EPMC12699241 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature